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Deep Reinforcement Learning Integrated PID for Hybrid Adaptive Control Approach in Automatic Voltage Regulation Systems.

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Title: Deep Reinforcement Learning Integrated PID for Hybrid Adaptive Control Approach in Automatic Voltage Regulation Systems.
Authors: Ali, Ahmed K.1 (AUTHOR), Al-Obaidi, Mudhar A.2 (AUTHOR), Ismail, Alhassan H.3 (AUTHOR), Mohammed, M. N.4 (AUTHOR), Sadeq, Abdellatif M.5 (AUTHOR) abdellatifsadeq23@gmail.com
Source: Energies (19961073). Jun2026, Vol. 19 Issue 11, p2693. 39p.
Subject Terms: *PID controllers, *Reinforcement learning, *Synchronous generators, *Voltage regulators, *Adaptive control systems, *Feedback control systems
Abstract: Automatic voltage regulation (AVR) systems play an important role in maintaining voltage stability, ensuring efficiency, and enhancing the reliability of synchronous generators. Although conventional PID (proportional-integral-derivative) controllers are widely adopted for AVR systems due to their simplicity and robustness, their performance is still limited under dynamic operating conditions. In this paper, this problem is addressed by developing an intelligent controller using a deep reinforcement learning (DRL)-based PID controller, which integrates PID with a reinforcement learning agent to create an adaptive intelligent controller for AVR systems. A comprehensive evaluation of AVR system performance under four control configurations is presented: (1) a conventional PID controller optimised using three recent hybrid optimisation algorithms, (2) a fractional-order proportional-integral-derivative (FOPID) controller tuned with the same hybrid algorithms, (3) a proposed DRL-based FOPID controller, and (4) a proposed DRL-based PID controller. The DRL-based PID controller parameters are adapted by using the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm, which allows the improvement of generalisation and adaptive learning. The simulation results demonstrate that the proposed DRL-FOPID controller significantly improves performance compared to both the conventional PID and conventional FOPID controllers that were tuned using a hybrid optimisation algorithm. The results emphasise the DRL-based controller in the development of intelligent controllers for AVR systems. [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
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Header DbId: enr
DbLabel: Energy & Power Source
An: 194588081
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Data: Deep Reinforcement Learning Integrated PID for Hybrid Adaptive Control Approach in Automatic Voltage Regulation Systems.
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  Data: <searchLink fieldCode="AR" term="%22Ali%2C+Ahmed+K%2E%22">Ali, Ahmed K.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Al-Obaidi%2C+Mudhar+A%2E%22">Al-Obaidi, Mudhar A.</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ismail%2C+Alhassan+H%2E%22">Ismail, Alhassan H.</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Mohammed%2C+M%2E+N%2E%22">Mohammed, M. N.</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Sadeq%2C+Abdellatif+M%2E%22">Sadeq, Abdellatif M.</searchLink><relatesTo>5</relatesTo> (AUTHOR)<i> abdellatifsadeq23@gmail.com</i>
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  Data: <searchLink fieldCode="JN" term="%22Energies+%2819961073%29%22">Energies (19961073)</searchLink>. Jun2026, Vol. 19 Issue 11, p2693. 39p.
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  Data: *<searchLink fieldCode="DE" term="%22PID+controllers%22">PID controllers</searchLink><br />*<searchLink fieldCode="DE" term="%22Reinforcement+learning%22">Reinforcement learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Synchronous+generators%22">Synchronous generators</searchLink><br />*<searchLink fieldCode="DE" term="%22Voltage+regulators%22">Voltage regulators</searchLink><br />*<searchLink fieldCode="DE" term="%22Adaptive+control+systems%22">Adaptive control systems</searchLink><br />*<searchLink fieldCode="DE" term="%22Feedback+control+systems%22">Feedback control systems</searchLink>
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  Label: Abstract
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  Data: Automatic voltage regulation (AVR) systems play an important role in maintaining voltage stability, ensuring efficiency, and enhancing the reliability of synchronous generators. Although conventional PID (proportional-integral-derivative) controllers are widely adopted for AVR systems due to their simplicity and robustness, their performance is still limited under dynamic operating conditions. In this paper, this problem is addressed by developing an intelligent controller using a deep reinforcement learning (DRL)-based PID controller, which integrates PID with a reinforcement learning agent to create an adaptive intelligent controller for AVR systems. A comprehensive evaluation of AVR system performance under four control configurations is presented: (1) a conventional PID controller optimised using three recent hybrid optimisation algorithms, (2) a fractional-order proportional-integral-derivative (FOPID) controller tuned with the same hybrid algorithms, (3) a proposed DRL-based FOPID controller, and (4) a proposed DRL-based PID controller. The DRL-based PID controller parameters are adapted by using the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm, which allows the improvement of generalisation and adaptive learning. The simulation results demonstrate that the proposed DRL-FOPID controller significantly improves performance compared to both the conventional PID and conventional FOPID controllers that were tuned using a hybrid optimisation algorithm. The results emphasise the DRL-based controller in the development of intelligent controllers for AVR systems. [ABSTRACT FROM AUTHOR]
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=enr&AN=194588081
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        Value: 10.3390/en19112693
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      – Code: eng
        Text: English
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      Pagination:
        PageCount: 39
        StartPage: 2693
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      – SubjectFull: PID controllers
        Type: general
      – SubjectFull: Reinforcement learning
        Type: general
      – SubjectFull: Synchronous generators
        Type: general
      – SubjectFull: Voltage regulators
        Type: general
      – SubjectFull: Adaptive control systems
        Type: general
      – SubjectFull: Feedback control systems
        Type: general
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      – TitleFull: Deep Reinforcement Learning Integrated PID for Hybrid Adaptive Control Approach in Automatic Voltage Regulation Systems.
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            NameFull: Ali, Ahmed K.
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            NameFull: Al-Obaidi, Mudhar A.
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            NameFull: Mohammed, M. N.
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            NameFull: Sadeq, Abdellatif M.
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            – D: 01
              M: 06
              Text: Jun2026
              Type: published
              Y: 2026
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              Value: 19
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            – TitleFull: Energies (19961073)
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